Information-Theoretic Constraints for Continual Vision-Language-Action Alignment

A team of researchers has introduced Info-VLA, a novel framework designed to solve a critical problem in robotics AI: catastrophic forgetting. When Vision-Language-Action (VLA) models—which enable robots to understand visual scenes, follow language instructions, and take actions—are continually trained on new tasks in open-ended environments, they typically degrade and forget previously learned skills. The paper identifies that this forgetting is linked to the deterioration of the intricate information structure connecting visual observations, language commands, and physical actions.

Info-VLA combats this by applying two complementary, information-theoretic constraints during continual learning. The first, Replay Anchor Contrastive Learning, uses a frozen 'teacher' model to create stable alignment anchors in the representation space, preserving how different modalities relate to each other. The second, Cross-Modal Mutual Information Maximization, actively maintains the dependency structure between visual and language representations by maximizing their mutual information. This dual approach allows the model to balance stability (retaining old knowledge) with plasticity (acquiring new skills).

In experiments on the challenging LIBERO benchmark for robotic task learning, Info-VLA demonstrated a significant performance leap over existing continual learning methods. The framework proved more effective at both retaining knowledge of past tasks and efficiently adapting to new ones, marking a substantial step toward creating lifelong learning machines. This work moves beyond traditional methods that fail to account for the complex, cross-modal nature of robotic perception and control.